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This session will introduce attendees to Python's rich ecosystem of abstract syntax tree tooling and libraries, with an emphasis on practical applications in static analysis and metaprogramming. Attendees should be fully comfortable with Python syntax and semantics, but familiarity with the ast module itself will not be necessary.

David Robinson did a nice writeup of using his R package to analyze who wrote the “I Am Part of the Resistance Inside the Trump Administration” op-ed in NYTimes. His approach was with TF-IDF of the words. I wanted to try this with different text statsistics of the linguistic features instead, since I’m guessing word usage will not give the author away. And in Python of course.

Reading and predicting what code will do is a fundamental coding skill. But when students read code are they executing it on their brain computer? Or do they only read the words?. This talk will explore learning to read and trace code, misconceptions and how to build a really good brain computer.

When first starting to learn how to optimise machine learning models I would often find, after getting to the model building stage, that I would have to keep going back to revisit the data to better handle the types of features present in the dataset. Over time I have found that one of the first steps to take before building the models is to carefully review the variable types present in the data, and to try to determine up front the best transformation process to take to achieve the optimal model performance.

One of the best ways to demonstrate the usefulness of the Pandas library is to use it to analyse financial data.In this notebook, we will compute a few financial measures with Pandas?—?returns, volatilities and Value at Risk, and visualise/plot these measures.

Jupyter notebooks are interactive documents that contain code, narratives, plots. They are an excellent place for experimenting with code and data. Notebooks are easily shared, and the 2.6M notebooks on GitHub just tell how popular notebooks are!

Linear Regression is the simplest form of machine learning out there. In this post, we will see how linear regression works and implement it in Python from scratch. This is the written version of the above video. Watch it if you prefer that.